The purpose of this project is to further develop a population- based policy model that is specific for the prevention, detection, and treatment of colorectal cancer in the United States. The model will be a dynamic Monte Carlo simulation of the U.S. population that will include all of the modifiable and non-modifiable risk factors that have been found in epidemiological studies to be associated with the incidence of colorectal cancer: smoking history, body mass index, physical activity, red meat consumption, fruit and vegetable consumption, aspirin use, multivitamin use (as a proxy for folate intake), alcohol consumption, postmenopausal hormone use, and family history of colorectal cancer, as well as demographic variables (age, gender, race). Risk equations will be derived primarily from the Nurses' Health Study and the Health Professionals Follow-up Study; risk factor distributions over time in the U.S. population will be obtained primarily from the NHANES I, II, and III. The model will track the underlying progression and location of adenomatous polyps and undiagnosed cancer, thus enabling a screening test to detect and remove an adenomatous polyp, or to possibly detect a cancer at an earlier stage. Once a cancer is detected and staged (either by a screening test or by symptoms), all relevant colorectal cancer treatment strategies will be incorporated, allowing for the evaluation of current or hypothetical interventions. The model will be used to analyze the potential contributors to the observed cancer trends and to predict the potential impact on national trends of risk factor interventions, screening, and colorectal cancer treatment. Throughout this project, the research team will collaborate with the National Cancer Institute to incorporate national data sources and to focus research questions. The research team will also be involved with other modeling groups for purposes of calibration, validation, and the comparison of model results.